Transcriptome Evolution among Species
If the majority of evolutionary changes are caused by historical accidents rather than by natural selection, they will accumulate mainly as a function of time rather than as a function of morphological or behavioral change of organisms. Applied to transcriptome evolution, a neutral model therefore implies that the rate of transcriptome change is proportional to time. In particular, if we assume that mutations cause changes in the relative amounts of transcripts independently of the absolute expression level of the gene, then the squared difference of the logarithm of the expression level is expected to increase linearly with divergence time (Lande 1976
; Felsenstein 2004
). To investigate whether this is the case, we have studied differences in the gene expression levels of around 12,000 genes in the prefrontal cortex of six humans, three chimpanzees (Pan trogodytes),
one orangutan (Pongo pygmaea),
and one rhesus macaque (Macaca mulatta)
using oligonucleotide microarrays. To exclude the influence of DNA sequence differences on the hybridization results, at least between humans and chimpanzees, only oligonucleotide probes that matched perfectly to the chimpanzee DNA sequences were used in the analysis (see Materials and Methods
). In A, we plot species divergence times against the average squared difference between the logarithm of the expression levels of 1,998 genes that had expression levels large enough to be detected in all primate samples. Although comparisons involving orangutan and rhesus were complicated by nucleotide sequence differences to array probes, the result shows that the squared differences represent an approximately linear function of time over at least 20 million years. When we apply the same analysis to published gene expression data for the livers of three humans, three chimpanzees, and one orangutan (Enard et al. 2002
), we again observe a linear relationship between gene expression differences and species divergence times (B).
Brain and Liver Transcriptome Change among Primates as a Function of Time
Since oligonucleotide-based microarrays are sensitive to DNA sequence differences and the orangutan and rhesus macaque genome sequences are not yet known—so that we cannot delete oligonucleotides carrying mismatches between the species—we used arrays containing around 28,000 cDNAs ranging in length from 500 to 1,500 nucleotides to assay gene expression patterns in the prefrontal cortex of six humans, five chimpanzees, five rhesus macaques, and five crab-eating macaques (Macaca fascicularis).
Due to the greater probe length, these arrays are much less sensitive to DNA sequence differences and therefore can be used to compare gene expression in humans and macaques (Ranz et al. 2003
). When we plot the extent of gene expression divergence for 5,829 genes whose expression was detected in all samples against species divergence time, we again observe that expression differences accumulate approximately linearly with time ().
Brain Transcriptome Change as Measured by cDNA Arrays
In a recent study of gene expression in the brains of humans, chimpanzees, and orangutans, we found that the rate of expression change on the human lineage has been larger than on the chimpanzee lineage (Enard et al. 2002
). This is in apparent contradiction to the linearity observed here. However, the analysis of Enard et al. (2002
) was based on less than 5% of all genes expressed in the brain because it was confined to genes that differed significantly in expression between humans and chimpanzees. In contrast, here we perform a transcriptome-wide analysis of all genes with detectable expression in several primate species. However, the slightly higher divergence of humans than chimpanzees from the two macaque species may reflect the previously reported higher rate of gene expression divergence on the human evolutionary lineage (Enard et al. 2002
; Caceres et al. 2003
; Gu and Gu 2003
). However, additional experiments are necessary to exclude the possibility that this is caused by experimental artifacts.
The clocklike accumulation of expression differences between species observed for primates is in agreement with the recent observation that differences in gene expression are consistent with phylogenetic relationships among Drosophila
species (Rifkin et al. 2003
), and both these observations are compatible with the predictions of the neutral model. However, under certain selection scenarios, positively selected changes would also accumulate linearly with time (Felsenstein 2004
). Therefore, linear accumulation of expression differences alone does not rule out selection.
In addition to the clocklike accumulation of evolutionary changes, the neutral theory states that the same forces determine the rate of evolution both within and between species (Kimura 1983
). Thus, a neutral prediction with respect to transcriptome evolution is that genes that vary more within species should be more likely to change between species as well. In order to test this, we ranked 2,926 genes with detectable expression levels in six humans and three chimpanzees according to their variation within humans and calculated the species divergences for the 25% of genes that had the largest and the smallest human variation, respectively. C shows that the genes with high variation among humans changed significantly faster between species than the genes with low variation. The magnitude of observed expression differences may be influenced by DNA sequence mismatches affecting hybridization between orangutan and rhesus samples and array probes. However, the difference in divergence rates between genes with high and low expression variation within species is unlikely to be explained by hybridization artifacts, since this would require a difference in sequence divergence between the two groups of genes.
We further considered the correlation between the average diversity within humans and chimpanzees and the divergence between the species for the 2,926 genes. This correlation is highly significant (p
< 0.001) as gauged by a permutation test (see Materials and Methods
). Since all array probes that carried sequence differences between humans and chimpanzees were removed prior to analysis, this correlation is not affected by hybridization artifacts. The strength of the correlation (τ = 0.24) is of a similar magnitude as the one obtained for the correlation of diversity and divergence of random genomic DNA sequences in humans and chimpanzees (τ = 0.179, p
= 0.028, n
= 76), the vast majority of which are noncoding (Hellmann et al. 2003
). Thus, although the two measures are not directly comparable, the degree of correlation between intraspecific diversity and interspecific divergence is similar for brain transcriptomes and random genomic DNA sequences in humans and chimpanzees.
To investigate whether gene expression differences accumulate as a function of time also in another group of mammals, we analyzed three mouse species. An advantage in this case is that post mortem artifacts are less likely to influence the results than in the case of autopsy material of humans and great apes. We determined differences in gene expression levels for around 9,000 genes in the frontal cortex of six outbred Mus musculus,
three outbred M. spretus,
and one M. caroli.
As shown in A, the squared transcriptome differences accumulated linearly with time among the mouse species. To test if divergence rates differ for the genes with high and low variation within species, we investigated the 25% of the 2,742 genes detected in all samples with the highest and the lowest variation within M. musculus,
respectively, as was done in the primates. B shows that genes that vary more within M. musculus
diverged faster among mouse species than genes that vary less. As in the case of primate species, imperfect matches of M. spretus
and M. caroli
mRNAs to the array oligonucleotides may partly influence the observed expression differences between species. Nonetheless, as for primates, the difference in divergence rates between genes with high and low expression variation within species is unlikely to be explained by hybridization differences since there is no indication that genes that vary more in expression within species diverge faster between species with respect to their DNA sequence. The correlation between diversity and divergence for M. musculus
and M. spretus
for genes detected in both species is highly significant (τ = 0.29, p
< 0.001, n
= 3,139), although in this case we cannot correct for DNA sequence differences. A correlation between gene expression differences within and between species was recently demonstrated also in teleost fish (Oleksiak et al. 2002
). Thus, in agreement with the neutral model, genes that vary more within species tend to vary more between species in three vertebrate groups.
Brain Transcriptome Change among Mice as a Function of Time
A Test for Neutrality
One way to test whether gene expression differences between species accumulate at a rate consistent with neutral expectation is to compare them to the expression differences observed for a class of genes that can reasonably be expected to not be the direct targets of positive or negative selection. Since expressed pseudogenes do not produce any functional gene products, they can be viewed as such a class of genes. Thus, if a substantial proportion of intact genes accumulate expression differences faster than pseudogenes, this would indicate that they are positively selected. Such an observation would falsify a neutral model.
To test this, we considered the expression patterns in four regions of the brain in three humans and three chimpanzees using the Affymetrix U95 array set interrogating approximately 40,000 genes (Philipp Khaitovich, unpublished data). In order to identify all probe sets on these arrays that interrogate expressed pseudogenes, we aligned the probe sequences, as well as published lists of human pseudogenes, to the human genome (see Materials and Methods
). In total, 889 probe sets that overlap with pseudogenes were identified. Thirty-three of these were detected (detection p
-value < 0.05) in at least one of four brain regions in either the chimpanzees or the humans after masking all probes carrying DNA sequence differences between the species. Of these, 28 contained at least one mutation that leads to a loss of function in both humans and chimpanzees. We therefore assumed that these pseudogenes were nonfunctional in the common ancestor of humans and chimpanzees. Finally, we checked whether these probe sets may crosshybridize with any intact genes by aligning them to the human genome. This left us with 23 expressed pseudogenes.
We compared the distributions of the squared differences between the mean expression levels of each gene in humans and in chimpanzees for the 23 pseudogenes and 12,647 intact genes for each of the four brain regions. In each case, only the genes detected in a given brain region were used for the calculation. In all four brain regions the distribution of expression distances among intact genes did not differ significantly from that among pseudogenes in either a Kolmogorov-Smirnov test or a Wilcoxon rank sum test. These tests would have been significant if more than 5% (1/23) of the genes had a distribution radically different from that of the pseudogenes. When the data for four brain regions were combined, no visual difference between the two distributions was apparent (p = 0.16 and p = 0.69, respectively) (A).
Comparison between Intact Genes and Pseudogenes
Thus, we failed to detect any significant excess of intact genes that diverged faster in expression than pseudogenes. This indicates that the fraction of gene expression differences between the species that are fixed by positive selection is small. Interestingly, there was also no detectable excess of intact genes that diverged slower than pseudogenes. This may seem unexpected, since the expression of many intact genes might be thought to be stabilized by negative selection and therefore to change more slowly than pseudogenes. This may indicate that purifying selection as well is a weak force affecting gene expression. However, it should be noted that the small number of expressed pseudogenes analyzed limits the power to detect positive and negative selection. A targeted effort to study expressed pseudogenes in closely related species would be a worthwhile undertaking.
A Test for Positive Selection
The fact that the overall accumulation of expression differences conforms to a selectively neutral model does not mean, of course, that all expression differences between species are selectively neutral. As for nucleotide changes, some changes in gene expression will have had phenotypic consequences and some of these will have become fixed due to positive selection. To identify such gene expression differences, we propose to use the ratio of divergence between species to diversity within species, akin to the tests suggested for quantitative genetic traits (Charlesworth 1984
; Lynch and Hill 1986
; Turelli et al. 1988
) and in agreement with recent suggestions by Rifkin et al. (2003)
or Hsieh et al. (2003
). However, to do this it is necessary for each gene considered to distinguish the gene expression diversity caused by genetic differences between individuals from the diversity caused by environmental factors. This is crucial since the environmental component is likely to be much larger than the genetic component. For example, under strict neutrality and no environmental influence, we expect a divergence to diversity ratio that is equal to the ratio of time of divergence of the species to the average time to the common ancestors of the individuals sampled within a species. This would be about 1:10 for humans and chimpanzees (Chen and Li 2001
; Lander et al. 2001
). However, the observed ratio is approximately 1:3, suggesting that the environmental component is on the order of three times bigger than the genetic component. Studies of gene expression differences among individuals with different genetic relatedness will eventually allow an estimation of the genetic component of expression variation.
Since we are unable to tease apart genetic and environmental contributions to expression diversity, we instead used pseudogenes to estimate the distribution of divergence to diversity ratios observed in the absence of selection and compared these ratios to intact genes. No significant difference was found (Kolmogorov-Smirnov test, p = 0.388; Wilcoxon rank sum test, p = 0.134), and both distributions appeared to center around roughly the same values (B). Note that this observation has to be taken cautiously since it is based on a small number of pseudogenes and the gene expression diversity is calculated from only three human individuals. Nevertheless, this result indicates that there is no drastic difference between the expression patterns of intact genes and expressed pseudogenes, since our tests would have been significant if 5% or more of the genes had had a radically different divergence to diversity ratio than that observed among the pseudogenes.
Transcriptome Evolution among Brain Regions
Different anatomical brain structures appeared at different times during vertebrate evolution. These time points can be viewed as divergence times between brain regions extending millions of years back in the past (A). If gene expression changes between different brain regions have a large random component, gene expression differences between brain regions within species could potentially be used as a molecular clock to time the divergences of tissues. To investigate whether this may be the case, we compared expression patterns for Brodmann's area 44, the prefrontal cortex, the anterior cingulate cortex, the primary visual cortex, the caudate nucleus, and the cerebellum in three adult human and three adult chimpanzee males (Philipp Khaitovich, unpublished data). All comparisons were performed between brain regions within the same individual. This has two advantages. First, such comparisons are unaffected by nucleotide sequence variation between and within species. Second, environmental differences and post mortem changes have little effect when expression differences within one individual are studied. In B, we plot the average squared distances between the six brain regions in humans and chimpanzees against the time when these brain regions emerged during vertebrate evolution (Butler and Hodos 1996
; Nieuwenhuys et al. 1998
) for 2,297 and 2,525 genes detected in all human and all chimpanzee samples, respectively. It can be seen that the expression differences increase approximately linearly with time over more than half a billion years. To investigate if this finding holds also in another mammalian species, we used published expression data for 1,346 genes with detectable expression in eight brain regions in the mouse (Su et al. 2002
). In this case as well there is an approximately linear relationship between transcriptome differences and evolutionary divergence times (C).
Transcriptome Change among Brain Regions as a Function of Evolutionary Time
If gene expression differences between the brain regions were largely adaptive, one would expect them to correlate with tissue function and not with evolutionary divergence time. Our data show that tissues that diverged recently have very similar gene expression profiles irrespective of the differences in function. For instance, the transcriptome of Brodmann's area 44 in the left hemisphere (Broca's area) is very similar to that of the prefrontal cortex in both humans and chimpanzees, although it is known to be involved in speech processing in humans while it must have another function in chimpanzees (Kandel et al. 2000
). This is what we would expect if the time since divergence rather than the extent of functional differences determined the magnitude of transcriptome change. Thus, although a number of expression differences between brain regions surely correspond to functional differences, our findings suggest that a sizeable proportion of the differences are functionally neutral.
A noteworthy finding is that the accumulation of expression differences between brain regions within a species is much slower than the accumulation of expression differences within a brain region between species. In fact, the expression differences that have accumulated among the primate species over 20 million years (see A) are approximately as extensive as those that have accumulated among brain regions over 500 million years (see B). This is likely to result from the fact that all expression differences seen between brain regions within an individual are caused by changes in regulatory networks established during development by cells that carry the same genome. In addition, expression differences between brain regions reflect the different cell-type compositions of these regions. In contrast, transcriptome differences between species are the result of changes in regulatory networks and cellular composition of tissues, as well as nucleotide sequence differences between species that affect promoters and other genomic elements that determine transcript levels. Our results show that the latter type of changes are much more common than the former.
A possible alternative explanation for the correlation between differences in gene expression and evolutionary divergence time among brain regions could be that differences in gene expression do not correlate with evolutionary divergence time, but instead with divergence time during fetal development. Our observations would then result from the fact that both developmental divergence times and expression differences correlate with evolutionary divergence. A correlation between developmental and evolutionary divergence times has been hypothesized before (for a review, see Gould 1977
). In fact, gene expression analyses now provide a quantitative approach to address this question and may also provide a tool to date the evolutionary emergence of brain regions that cannot be discerned in the fossil record.